In handwriting recognition, information gathered on characters handwritten onto a motion tracking surface or otherwise entered by hand can include spatial information. Spatial information can characterize the overall shape of a character input and/or a portion of the character input. Conventionally, the spatial information of a character input is extracted from a bitmap of the input.
One difficulty in performing handwriting recognition is that the production of handwritten characters is subject to a range of artifacts reflecting the variability of human behavior. For example, an input stroke that is intended to be horizontal is, in fact, very unlikely to be perfectly horizontal. Instead, the stroke may display a slight slant, either due to the particular angle of the writing device or the angle of the user's finger. The input stroke may comprise short “hooks” in random directions at the beginning and/or at the end of the stroke. Depending on how the finger or other writing accessory (e.g., stylus) landed on and left the writing surface, it may show little wiggles in places, caused by uneven motor control and it may even be briefly interrupted if the finger or other writing accessory briefly lost contact with the writing surface.
To smooth out such artifacts, conventionally, the resolution of the device receiving the handwritten characters is lowered, for example, by utilizing a coarser bitmap. For example, for a device with a native 960-by-640-pixel resolution, the 32-by-32 bitmap may be chosen (e.g., by a designer of the handwriting recognition technique). However, one drawback to selecting a coarser bitmap is that while the lower resolution helps to ignore some of the artifacts, it may also obscure important details of the input character, which could be critical to disambiguate between certain words or characters. This is especially relevant when it comes to the recognition of Chinese characters because of the inherent complexity of characters with many strokes (e.g., more than 20 strokes is not all that uncommon, particularly with named entities), and the fact that sometimes only one short stroke is the key to disambiguating between two Chinese characters.
In order to perform handwriting recognition using feature extraction, features related to the spatial aspects of a character are extracted from the input. Typically, spatial features tend to include variations on pixel-level chain (or stroke) codes, sector occupancy, and the Rutovitz crossing number. Though the exact size of the bitmap may vary, it is typically chosen a priori and subsequently used throughout the feature extraction. As a result, spatial features are obtained at a single, fixed resolution determined by the size of the bitmap.